Analytics Lab PhD Scholarships
Architectural design, engineering, and construction businesses are major creators and users of building and infrastructure-related data. This data is embedded in multi-dimensional digital files and models of buildings (including 3D forms, 4D timelines, 5D scheduling). The Analytics Lab develops methods for harvesting and leveraging the data that architectural design, engineering, and construction businesses use and create. Big data analysis will be used to extrapolate future patterns of scenarios, providing a basis for developing new knowledge and models (replacing ‘conventional’ materials with composites, for example). Both approaches are underpinned by cybersecurity provisions for file exchange between digital twins and digital fabrication.
Desired disciplinary backgrounds include: data science, computer science, data analytics, business strategy, architecture, engineering, construction management, computational design.
Desired abilities and technical skills include at least one of the following skills: Grasshopper, Rhino, AutoCAD, Revit, programming Languages suitable for ML/AI, advanced understanding of data cleaning and structuring in appropriate data bases, advanced knowledge on ML/AI. The Research
Analytics Topic 1
How is design knowledge embedded in Architecture, Engineering, and Construction sector data (BIM, CAD, etc.) and what methods (for example, artificial intelligence, machine learning) are available for harvesting design data embedded in past projects?
This topic may include consideration of: the usefulness of design data and its security; along with legal, ethical, and commercial challenges to using design data.
Analytics Topic 2
How can design data (BIM, CAD, etc.) be used to support data-intensive scenario planning (predictive modelling) for the Architecture, Engineering, and Construction Sector? What methods are available for using predictive modelling to help architects during the design process?
This topic may include consideration of: cognitive, cultural, and social factors when using data in the design process.
Analytics Topic 3
How should practice data (BIM, GIS, Lidar models, etc.) be structured to enable knowledge accessibility, interoperability, and security? How should errors, risks, and file be sharing handled?
This topic may include consideration of: vocabulary and semantics/ontology across architectural sectors and how it is covered by standards; the impacts of interfaces and communication methods needed to feed analysed practice data back to practice.
Analytics Topic 4
How is design data stored, made accessible, or secure, and what are the practical, legal, ethical, and commercial frameworks that allow or prevent the use of architectural data?
This topic may include consideration of: the potential benefits and limits of current practice, the relevant timescales (years / decades) for different data uses (circular economy) and types of users (individual/society).
Analytics Topic 5
How might architects employ new technology (Internet of Things, Virtual Reality, Augmented Reality, Multiple Realities, Artificial Intelligence, Machine Learning) to articulate their value proposition and contribution in a context of increased specialisation?
This topic may include consideration of opportunities for leveraging IoT/VR/AR/MR/AI/ML for performance analysis in Design for Fabrication.
Analytics Topic 6
What are the barriers to architects adopting new technology, and what are the implications for individuals, teams, and managers when a firm adopts a new technology?
This topic covers issues of: accessibility, training, communication, and cost of the use of technology for design and fabrication; and may include visualising social connections and interfaces to support technology adoption in architectural manufacturing.
Analytics Topic 7
What digital twin concepts exist outside the Architecture, Engineering, and Construction sector and how can they assist a simulation of architectural manufacturing prior to construction?
This topic may include consideration of: the value propositions for simulating manufacturing during the design process, as well as the infrastructure and resources needed for computationally intensive simulation of architectural manufacturing.